Code-Switching: A Useful Foreign Language Teaching Tool in EFL Classrooms
Bibliographic record
Abstract
In every society, language plays a vital role in communicating with each other as it allows speakers to expand their knowledge, deliver their ideas, opinions and feelings in the society. English, as a global language, provides a platform for communication for people who speak the language. Due to the growing trend in linguistic globalisation, bilingualism has become a very common phenomenon in today’s world. In bilingual communities all over the world, speakers frequently switch from one language to another to meet communication demands. This phenomenon of alternation between languages is known as code-switching. The present study aims to focus on the teachers’ use of code-switching as a language teaching tool in EFL classrooms in Pakistan. It also deals with the functions and types of code-switching in EFL classrooms. Four EFL speaking skill classes were observed, and audio was recorded and transcribed to analyse why and how code-switching was used in the classrooms. The analysis of classroom interaction transcripts revealed that teachers code-switched to maintain discipline, translate new words and build solidarity and intimate relationships with the students before, during and after the lessons. The study found that code-switching from L2 to L1 in the speaking classes did occur although English remained as the main medium of instruction. All the teachers consciously code-switched throughout their lectures. Teachers also code switched to Urdu after the lectures. Three types of code-switching occurred during the EFL classes: tag-switching, intra-sentential code-switching and inter-sentential switching. Hence, code-switching is a useful teaching tool in EFL classrooms to facilitate teaching and learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".